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[2023-09-02 17:17:15,252::train::INFO] [train] Iter 12374 | loss 1.5135 | loss(rot) 0.4776 | loss(pos) 0.6421 | loss(seq) 0.3938 | grad 5.1329 | lr 0.0010 | time_forward 13.3830 | time_backward 6.0640 |
[2023-09-02 17:17:25,863::train::INFO] [train] Iter 12375 | loss 1.6949 | loss(rot) 1.5445 | loss(pos) 0.1473 | loss(seq) 0.0031 | grad 7.2333 | lr 0.0010 | time_forward 5.1270 | time_backward 5.4810 |
[2023-09-02 17:17:36,767::train::INFO] [train] Iter 12376 | loss 0.9067 | loss(rot) 0.3044 | loss(pos) 0.5264 | loss(seq) 0.0758 | grad 3.9961 | lr 0.0010 | time_forward 4.1130 | time_backward 5.9730 |
[2023-09-02 17:17:44,686::train::INFO] [train] Iter 12377 | loss 1.4809 | loss(rot) 0.6715 | loss(pos) 0.2526 | loss(seq) 0.5568 | grad 4.5775 | lr 0.0010 | time_forward 3.3360 | time_backward 4.5800 |
[2023-09-02 17:17:53,719::train::INFO] [train] Iter 12378 | loss 1.4531 | loss(rot) 1.0669 | loss(pos) 0.3428 | loss(seq) 0.0433 | grad 6.7736 | lr 0.0010 | time_forward 3.8920 | time_backward 5.1370 |
[2023-09-02 17:17:56,934::train::INFO] [train] Iter 12379 | loss 1.2271 | loss(rot) 0.2999 | loss(pos) 0.7609 | loss(seq) 0.1663 | grad 5.3228 | lr 0.0010 | time_forward 1.3850 | time_backward 1.8270 |
[2023-09-02 17:18:06,979::train::INFO] [train] Iter 12380 | loss 0.7118 | loss(rot) 0.1077 | loss(pos) 0.5936 | loss(seq) 0.0105 | grad 4.9769 | lr 0.0010 | time_forward 4.1140 | time_backward 5.9270 |
[2023-09-02 17:18:09,591::train::INFO] [train] Iter 12381 | loss 1.6046 | loss(rot) 0.8646 | loss(pos) 0.1670 | loss(seq) 0.5731 | grad 5.1921 | lr 0.0010 | time_forward 1.2270 | time_backward 1.3820 |
[2023-09-02 17:18:13,527::train::INFO] [train] Iter 12382 | loss 1.7531 | loss(rot) 1.3210 | loss(pos) 0.4138 | loss(seq) 0.0183 | grad 7.0580 | lr 0.0010 | time_forward 1.2960 | time_backward 1.4160 |
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